from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 33.672632 |
| daal4py_KNeighborsClassifier | 0.0 | 5.0 | 11.022916 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 0.327172 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 46.091624 |
| KMeans_tall | 0.0 | 1.0 | 45.454342 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 19.288143 |
| KMeans_short | 0.0 | 0.0 | 23.942625 |
| daal4py_KMeans_short | 0.0 | 0.0 | 13.709325 |
| LogisticRegression | 0.0 | 1.0 | 8.222998 |
| daal4py_LogisticRegression | 0.0 | 1.0 | 0.455939 |
| Ridge | 0.0 | 0.0 | 26.110131 |
| daal4py_Ridge | 0.0 | 0.0 | 8.732427 |
| total | 0.0 | 32.0 | 57.122206 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.132 | 0.004 | 1000000 | 1000000 | 100 | -1 | 1 | 6.073 | NaN | 0.987 | 0.981 | 0.484 | 0.018 | 0.272 | 0.013 | See |
| 1 | KNeighborsClassifier | predict | 30.689 | 0.000 | 1000000 | 1000 | 100 | -1 | 1 | 0.000 | 0.031 | 0.987 | 0.981 | 3.787 | 0.015 | 8.104 | 0.032 | See |
| 2 | KNeighborsClassifier | predict | 0.191 | 0.016 | 1000000 | 1 | 100 | -1 | 1 | 0.004 | 0.000 | 0.987 | 0.981 | 0.100 | 0.002 | 1.913 | 0.167 | See |
| 3 | KNeighborsClassifier | fit | 0.132 | 0.002 | 1000000 | 1000000 | 100 | -1 | 5 | 6.060 | NaN | 0.987 | 0.981 | 0.490 | 0.011 | 0.269 | 0.007 | See |
| 4 | KNeighborsClassifier | predict | 38.225 | 0.000 | 1000000 | 1000 | 100 | -1 | 5 | 0.000 | 0.038 | 0.987 | 0.981 | 3.788 | 0.011 | 10.090 | 0.028 | See |
| 5 | KNeighborsClassifier | predict | 0.199 | 0.013 | 1000000 | 1 | 100 | -1 | 5 | 0.004 | 0.000 | 0.987 | 0.981 | 0.102 | 0.004 | 1.943 | 0.150 | See |
| 6 | KNeighborsClassifier | fit | 0.129 | 0.003 | 1000000 | 1000000 | 100 | -1 | 100 | 6.197 | NaN | 0.987 | 0.981 | 0.485 | 0.013 | 0.266 | 0.009 | See |
| 7 | KNeighborsClassifier | predict | 38.085 | 0.000 | 1000000 | 1000 | 100 | -1 | 100 | 0.000 | 0.038 | 0.987 | 0.981 | 3.852 | 0.016 | 9.888 | 0.040 | See |
| 8 | KNeighborsClassifier | predict | 0.199 | 0.011 | 1000000 | 1 | 100 | -1 | 100 | 0.004 | 0.000 | 0.987 | 0.981 | 0.100 | 0.002 | 1.985 | 0.121 | See |
| 9 | KNeighborsClassifier | fit | 0.140 | 0.002 | 1000000 | 1000000 | 100 | 1 | 1 | 5.726 | NaN | 0.987 | 0.981 | 0.480 | 0.012 | 0.291 | 0.009 | See |
| 10 | KNeighborsClassifier | predict | 15.145 | 0.162 | 1000000 | 1000 | 100 | 1 | 1 | 0.000 | 0.015 | 0.987 | 0.981 | 3.787 | 0.021 | 3.999 | 0.048 | See |
| 11 | KNeighborsClassifier | predict | 0.192 | 0.006 | 1000000 | 1 | 100 | 1 | 1 | 0.004 | 0.000 | 0.987 | 0.981 | 0.101 | 0.003 | 1.900 | 0.077 | See |
| 12 | KNeighborsClassifier | fit | 0.123 | 0.002 | 1000000 | 1000000 | 100 | 1 | 5 | 6.529 | NaN | 0.987 | 0.981 | 0.483 | 0.017 | 0.254 | 0.010 | See |
| 13 | KNeighborsClassifier | predict | 24.903 | 0.146 | 1000000 | 1000 | 100 | 1 | 5 | 0.000 | 0.025 | 0.987 | 0.981 | 3.784 | 0.010 | 6.581 | 0.042 | See |
| 14 | KNeighborsClassifier | predict | 0.203 | 0.008 | 1000000 | 1 | 100 | 1 | 5 | 0.004 | 0.000 | 0.987 | 0.981 | 0.101 | 0.003 | 2.018 | 0.093 | See |
| 15 | KNeighborsClassifier | fit | 0.130 | 0.002 | 1000000 | 1000000 | 100 | 1 | 100 | 6.148 | NaN | 0.987 | 0.981 | 0.500 | 0.010 | 0.260 | 0.006 | See |
| 16 | KNeighborsClassifier | predict | 24.747 | 0.062 | 1000000 | 1000 | 100 | 1 | 100 | 0.000 | 0.025 | 0.987 | 0.981 | 3.849 | 0.023 | 6.429 | 0.042 | See |
| 17 | KNeighborsClassifier | predict | 0.216 | 0.012 | 1000000 | 1 | 100 | 1 | 100 | 0.004 | 0.000 | 0.987 | 0.981 | 0.099 | 0.002 | 2.185 | 0.131 | See |
| 18 | KNeighborsClassifier | fit | 0.057 | 0.000 | 1000000 | 1000000 | 2 | -1 | 1 | 0.281 | NaN | 0.987 | 0.981 | 0.103 | 0.003 | 0.555 | 0.017 | See |
| 19 | KNeighborsClassifier | predict | 23.739 | 0.066 | 1000000 | 1000 | 2 | -1 | 1 | 0.000 | 0.024 | 0.987 | 0.981 | 0.799 | 0.008 | 29.700 | 0.326 | See |
| 20 | KNeighborsClassifier | predict | 0.020 | 0.001 | 1000000 | 1 | 2 | -1 | 1 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.000 | 4.738 | 0.518 | See |
| 21 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | -1 | 5 | 0.280 | NaN | 0.987 | 0.981 | 0.103 | 0.005 | 0.553 | 0.030 | See |
| 22 | KNeighborsClassifier | predict | 31.803 | 0.000 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.032 | 0.987 | 0.981 | 0.810 | 0.023 | 39.241 | 1.098 | See |
| 23 | KNeighborsClassifier | predict | 0.029 | 0.003 | 1000000 | 1 | 2 | -1 | 5 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.001 | 6.624 | 1.334 | See |
| 24 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | -1 | 100 | 0.281 | NaN | 0.987 | 0.981 | 0.103 | 0.002 | 0.551 | 0.011 | See |
| 25 | KNeighborsClassifier | predict | 31.374 | 0.000 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.031 | 0.987 | 0.981 | 0.868 | 0.009 | 36.143 | 0.370 | See |
| 26 | KNeighborsClassifier | predict | 0.028 | 0.001 | 1000000 | 1 | 2 | -1 | 100 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.000 | 6.349 | 0.622 | See |
| 27 | KNeighborsClassifier | fit | 0.058 | 0.003 | 1000000 | 1000000 | 2 | 1 | 1 | 0.275 | NaN | 0.987 | 0.981 | 0.104 | 0.003 | 0.560 | 0.030 | See |
| 28 | KNeighborsClassifier | predict | 10.521 | 0.039 | 1000000 | 1000 | 2 | 1 | 1 | 0.000 | 0.011 | 0.987 | 0.981 | 0.797 | 0.009 | 13.201 | 0.161 | See |
| 29 | KNeighborsClassifier | predict | 0.016 | 0.001 | 1000000 | 1 | 2 | 1 | 1 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.000 | 3.685 | 0.360 | See |
| 30 | KNeighborsClassifier | fit | 0.058 | 0.003 | 1000000 | 1000000 | 2 | 1 | 5 | 0.278 | NaN | 0.987 | 0.981 | 0.103 | 0.002 | 0.556 | 0.036 | See |
| 31 | KNeighborsClassifier | predict | 18.631 | 0.014 | 1000000 | 1000 | 2 | 1 | 5 | 0.000 | 0.019 | 0.987 | 0.981 | 0.802 | 0.010 | 23.223 | 0.285 | See |
| 32 | KNeighborsClassifier | predict | 0.022 | 0.001 | 1000000 | 1 | 2 | 1 | 5 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.000 | 5.305 | 0.476 | See |
| 33 | KNeighborsClassifier | fit | 0.058 | 0.001 | 1000000 | 1000000 | 2 | 1 | 100 | 0.278 | NaN | 0.987 | 0.981 | 0.104 | 0.003 | 0.556 | 0.020 | See |
| 34 | KNeighborsClassifier | predict | 18.577 | 0.020 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.019 | 0.987 | 0.981 | 0.865 | 0.007 | 21.472 | 0.169 | See |
| 35 | KNeighborsClassifier | predict | 0.022 | 0.001 | 1000000 | 1 | 2 | 1 | 100 | 0.001 | 0.000 | 0.987 | 0.981 | 0.004 | 0.000 | 5.168 | 0.421 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 2.821 | 0.034 | 1000000 | 1000000 | 10 | -1 | 1 | 0.028 | NaN | 0.986 | 0.987 | 0.741 | 0.012 | 3.807 | 0.079 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.414 | 0.009 | 1000000 | 1000 | 10 | -1 | 1 | 0.000 | 0.000 | 0.986 | 0.987 | 0.116 | 0.004 | 3.573 | 0.146 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.002 | 1000000 | 1 | 10 | -1 | 1 | 0.015 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 25.850 | 14.737 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 2.839 | 0.045 | 1000000 | 1000000 | 10 | -1 | 5 | 0.028 | NaN | 0.986 | 0.987 | 0.745 | 0.007 | 3.810 | 0.070 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.805 | 0.011 | 1000000 | 1000 | 10 | -1 | 5 | 0.000 | 0.001 | 0.986 | 0.987 | 0.212 | 0.002 | 3.804 | 0.062 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 10 | -1 | 5 | 0.023 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 7.227 | 3.438 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 2.857 | 0.041 | 1000000 | 1000000 | 10 | -1 | 100 | 0.028 | NaN | 0.986 | 0.987 | 0.700 | 0.006 | 4.080 | 0.068 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.590 | 0.020 | 1000000 | 1000 | 10 | -1 | 100 | 0.000 | 0.003 | 0.986 | 0.987 | 0.646 | 0.012 | 4.008 | 0.080 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.006 | 0.001 | 1000000 | 1 | 10 | -1 | 100 | 0.014 | 0.000 | 0.986 | 0.987 | 0.003 | 0.001 | 1.723 | 0.577 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 2.878 | 0.030 | 1000000 | 1000000 | 10 | 1 | 1 | 0.028 | NaN | 0.986 | 0.987 | 0.748 | 0.014 | 3.849 | 0.083 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.754 | 0.015 | 1000000 | 1000 | 10 | 1 | 1 | 0.000 | 0.001 | 0.986 | 0.987 | 0.117 | 0.004 | 6.432 | 0.250 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.001 | 1000000 | 1 | 10 | 1 | 1 | 0.053 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 8.138 | 5.041 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 2.847 | 0.032 | 1000000 | 1000000 | 10 | 1 | 5 | 0.028 | NaN | 0.986 | 0.987 | 0.700 | 0.008 | 4.065 | 0.064 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.428 | 0.019 | 1000000 | 1000 | 10 | 1 | 5 | 0.000 | 0.001 | 0.986 | 0.987 | 0.210 | 0.003 | 6.807 | 0.136 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | 1 | 5 | 0.049 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 3.364 | 1.443 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 2.835 | 0.035 | 1000000 | 1000000 | 10 | 1 | 100 | 0.028 | NaN | 0.986 | 0.987 | 0.745 | 0.006 | 3.805 | 0.058 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.690 | 0.030 | 1000000 | 1000 | 10 | 1 | 100 | 0.000 | 0.005 | 0.986 | 0.987 | 0.656 | 0.010 | 7.147 | 0.121 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | 1 | 100 | 0.019 | 0.000 | 0.986 | 0.987 | 0.002 | 0.001 | 1.751 | 0.613 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 0.772 | 0.004 | 1000000 | 1000000 | 2 | -1 | 1 | 0.021 | NaN | 0.986 | 0.987 | 0.479 | 0.005 | 1.610 | 0.019 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.033 | 0.001 | 1000000 | 1000 | 2 | -1 | 1 | 0.000 | 0.000 | 0.986 | 0.987 | 0.001 | 0.000 | 36.190 | 19.780 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 2 | -1 | 1 | 0.006 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 14.157 | 6.474 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 0.772 | 0.008 | 1000000 | 1000000 | 2 | -1 | 5 | 0.021 | NaN | 0.986 | 0.987 | 0.483 | 0.006 | 1.598 | 0.027 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.035 | 0.001 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.000 | 0.986 | 0.987 | 0.001 | 0.000 | 29.759 | 6.300 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 5 | 0.006 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 13.773 | 6.072 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 0.778 | 0.009 | 1000000 | 1000000 | 2 | -1 | 100 | 0.021 | NaN | 0.986 | 0.987 | 0.478 | 0.008 | 1.629 | 0.032 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.057 | 0.003 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.000 | 0.986 | 0.987 | 0.008 | 0.001 | 7.428 | 0.719 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | -1 | 100 | 0.006 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 15.367 | 8.977 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 0.776 | 0.008 | 1000000 | 1000000 | 2 | 1 | 1 | 0.021 | NaN | 0.986 | 0.987 | 0.485 | 0.007 | 1.600 | 0.028 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.032 | 0.001 | 1000000 | 1000 | 2 | 1 | 1 | 0.001 | 0.000 | 0.986 | 0.987 | 0.001 | 0.000 | 38.693 | 11.074 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 1 | 0.019 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 5.232 | 2.594 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 0.775 | 0.009 | 1000000 | 1000000 | 2 | 1 | 5 | 0.021 | NaN | 0.986 | 0.987 | 0.479 | 0.005 | 1.618 | 0.025 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.034 | 0.001 | 1000000 | 1000 | 2 | 1 | 5 | 0.000 | 0.000 | 0.986 | 0.987 | 0.001 | 0.001 | 25.703 | 12.070 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 5 | 0.019 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 2.670 | 1.927 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 0.775 | 0.005 | 1000000 | 1000000 | 2 | 1 | 100 | 0.021 | NaN | 0.986 | 0.987 | 0.488 | 0.016 | 1.586 | 0.052 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.058 | 0.003 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.000 | 0.986 | 0.987 | 0.007 | 0.001 | 7.990 | 0.800 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 100 | 0.018 | 0.000 | 0.986 | 0.987 | 0.000 | 0.000 | 5.170 | 2.518 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.617 | 0.014 | 1000000 | 1000000 | 2 | k-means++ | 30 | 0.778 | NaN | 0.002 | 30 | 0.002 | 0.310 | 0.010 | 1.991 | 0.080 | See |
| 1 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 2 | k-means++ | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 7.069 | 2.799 | See |
| 2 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | k-means++ | 30 | 0.010 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 9.187 | 4.130 | See |
| 3 | KMeans_tall | fit | 0.524 | 0.010 | 1000000 | 1000000 | 2 | random | 30 | 0.916 | NaN | 0.002 | 30 | 0.002 | 0.269 | 0.005 | 1.947 | 0.053 | See |
| 4 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 2 | random | 30 | 0.010 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 7.200 | 3.142 | See |
| 5 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | random | 30 | 0.010 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 5.523 | 2.430 | See |
| 6 | KMeans_tall | fit | 7.051 | 0.093 | 1000000 | 1000000 | 100 | k-means++ | 30 | 3.404 | NaN | 0.002 | 30 | 0.002 | 3.842 | 0.036 | 1.835 | 0.030 | See |
| 7 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | k-means++ | 30 | 0.463 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 5.336 | 1.996 | See |
| 8 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 100 | k-means++ | 30 | 0.518 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 8.067 | 3.154 | See |
| 9 | KMeans_tall | fit | 6.505 | 0.048 | 1000000 | 1000000 | 100 | random | 30 | 3.690 | NaN | 0.002 | 30 | 0.002 | 3.590 | 0.046 | 1.812 | 0.027 | See |
| 10 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | random | 30 | 0.452 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 5.448 | 1.883 | See |
| 11 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 100 | random | 30 | 0.529 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 7.095 | 2.970 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.318 | 0.010 | 10000 | 10000 | 2 | k-means++ | 27 | 0.014 | NaN | 0.006 | 25 | 0.007 | 0.144 | 0.007 | 2.212 | 0.130 | See |
| 1 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1000 | 2 | k-means++ | 27 | 0.008 | 0.0 | 0.006 | 25 | 0.007 | 0.001 | 0.000 | 2.956 | 1.153 | See |
| 2 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 2 | k-means++ | 27 | 0.011 | 0.0 | 0.006 | 25 | 0.007 | 0.000 | 0.000 | 8.637 | 4.314 | See |
| 3 | KMeans_short | fit | 0.131 | 0.001 | 10000 | 10000 | 2 | random | 30 | 0.037 | NaN | 0.006 | 30 | 0.007 | 0.070 | 0.004 | 1.876 | 0.101 | See |
| 4 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | random | 30 | 0.008 | 0.0 | 0.006 | 30 | 0.007 | 0.001 | 0.000 | 3.064 | 0.524 | See |
| 5 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1 | 2 | random | 30 | 0.008 | 0.0 | 0.006 | 30 | 0.007 | 0.000 | 0.000 | 12.365 | 7.315 | See |
| 6 | KMeans_short | fit | 1.051 | 0.054 | 10000 | 10000 | 100 | k-means++ | 20 | 0.152 | NaN | 0.006 | 21 | 0.007 | 0.613 | 0.036 | 1.715 | 0.135 | See |
| 7 | KMeans_short | predict | 0.004 | 0.001 | 10000 | 1000 | 100 | k-means++ | 20 | 0.226 | 0.0 | 0.006 | 21 | 0.007 | 0.001 | 0.000 | 2.427 | 0.874 | See |
| 8 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | k-means++ | 20 | 0.499 | 0.0 | 0.006 | 21 | 0.007 | 0.000 | 0.000 | 6.989 | 3.897 | See |
| 9 | KMeans_short | fit | 0.327 | 0.040 | 10000 | 10000 | 100 | random | 22 | 0.539 | NaN | 0.006 | 19 | 0.007 | 0.287 | 0.053 | 1.138 | 0.253 | See |
| 10 | KMeans_short | predict | 0.003 | 0.000 | 10000 | 1000 | 100 | random | 22 | 0.264 | 0.0 | 0.006 | 19 | 0.007 | 0.001 | 0.000 | 2.082 | 0.209 | See |
| 11 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | random | 22 | 0.479 | 0.0 | 0.006 | 19 | 0.007 | 0.000 | 0.000 | 8.257 | 3.478 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | class_weight | l1_ratio | n_jobs | random_state | n_iter | throughput | latency | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 15.250 | 0.035 | 1000000 | 1000000 | 100 | NaN | NaN | NaN | NaN | [20] | [-0.07737027] | NaN | 0.3 | 15.182 | 0.032 | 1.004 | 0.003 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | NaN | NaN | [20] | 2.2430589933545066 | 0.0 | 0.3 | 0.000 | 0.000 | 0.873 | 0.375 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | NaN | NaN | [20] | 8.208596861893794 | 0.0 | 0.3 | 0.000 | 0.000 | 0.383 | 0.312 | See |
| 3 | LogisticRegression | fit | 1.135 | 0.027 | 1000 | 1000 | 10000 | NaN | NaN | NaN | NaN | [26] | [1.83291975] | NaN | 0.3 | 1.137 | 0.046 | 0.998 | 0.046 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | NaN | NaN | NaN | NaN | [26] | 3.4440048507013428 | 0.0 | 0.3 | 0.004 | 0.000 | 0.531 | 0.089 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | NaN | NaN | [26] | 42.88831345827943 | 0.0 | 0.3 | 0.001 | 0.000 | 0.226 | 0.300 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | max_iter | random_state | throughput | latency | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 0.278 | 0.005 | 1000 | 1000 | 10000 | NaN | NaN | 0.288 | NaN | 1.0 | 0.285 | 0.002 | 0.976 | 0.020 | See |
| 1 | Ridge | predict | 0.011 | 0.000 | 1000 | 1000 | 10000 | NaN | NaN | 7.206 | 0.0 | 1.0 | 0.018 | 0.001 | 0.629 | 0.037 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | 708.529 | 0.0 | 1.0 | 0.000 | 0.000 | 0.724 | 0.503 | See |
| 3 | Ridge | fit | 1.177 | 0.044 | 1000000 | 1000000 | 100 | NaN | NaN | 0.680 | NaN | 1.0 | 0.328 | 0.002 | 3.586 | 0.136 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | 4.104 | 0.0 | 1.0 | 0.000 | 0.000 | 0.801 | 0.440 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | 8.687 | 0.0 | 1.0 | 0.000 | 0.000 | 0.642 | 0.551 | See |
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